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A FUZZY ADAPTIVE TEACHING

LEARNING-BASED OPTIMIZATION

STRATEGY FOR GENERATING MIXED

STRENGTH T-WAY TEST SUITES

FAKHRUD DIN

DOCTOR OF PHILOSOPHY

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SUPERVISOR’S DECLARATION

I hereby declare that I have checked this thesis and in my opinion, this thesis is adequate in terms of scope and quality for the award of the degree of Doctor of Philosophy.

_______________________________ (Supervisor’s Signature)

Full Name : PROF. DR. KAMAL ZUHAIRI BIN ZAMLI Position : PROFESSOR

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STUDENT’S DECLARATION

I hereby declare that the work in this thesis is based on my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously or concurrently submitted for any other degree at Universiti Malaysia Pahang or any other institutions.

_______________________________ (Student’s Signature)

Full Name : FAKHRUD DIN ID Number : PCC16010 Date :

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A FUZZY ADAPTIVE TEACHING LEARNING-BASED OPTIMIZATION

STRATEGY FOR GENERATING MIXED STRENGTH T-WAY TEST SUITES

FAKHRUD DIN

Thesis submitted in fulfillment of the requirements for the award of the degree of

Doctor of Philosophy

Faculty of Computer Systems & Software Engineering UNIVERSITI MALAYSIA PAHANG

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DEDICATION

Dedicated to my beloved parents and family.

For their countless prayers, endless love, unconditional support, tireless patience and continuous encouragement.

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ACKNOWLEDGEMENTS

Thanks to All Mighty Allah, the Magnificent, the Most Merciful Who always bestows upon me His endless blessings. The completion of this thesis is yet another very special blessing that All Mighty Allah bestowed on me. Indeed, ‘Then which of the favors of your Lord will you deny?’.

I am thankful to my supervisor, Professor Dr. Kamal Zuhairi Zamli, for his trust, patience and professional supervision. Without any doubt, his agile and goal-oriented supervision helped me to be an independent and productive researcher. I always got motivation and learned new research techniques whenever I met him. This work was only a dream without his novel style of supervision and dedication towards undertaking quality research. Thank you very much Prof! I consider myself very lucky to have you as my supervisor.

I am very grateful to my respectable parents for their kind prayers, selfless love and continuous support. I still remember my mother’s sleepless nights she spent with me whenever I fell ill. I never have a hard time in my life because of my father’s lifelong hard work. Khan G, you are my real hero! My parents are the best parents in the world. I am very thankful to my dearest wife for her unconditional support and company. I always feel blessed to have you in my life. Your presence and help made my Ph.D. journey very comfortable. Thank you very much, you are indeed a true, lovely and sincere partner. My special thanks go to my brothers and sisters, especially to my elder brother Mian Tufail Mohammad for his time and support for our family. I really love you all. I really appreciate the kind prayers of my mother-in-law and father-in-law. Finally, I would like to thank my entire family for their sincere prayers.

I would like to express my gratitude to my dear friends Captain Muhammad Sohail, Dr. Shah Khalid, Mr. Sami Ullah, Dr. Sami Ur Rahman, Mr. Mansoor Ahmed and Mr. Gohar Ali for their concerns and continuous support during my Ph.D. Thank you guys for being there for me always. I am very thankful to Mr. Riaz Ul Haq for his time and sincere help. I would like to thank Dr. Gran Badshah, Dr. Shahid Anwar, Dr. Mushtaq Ali and Mr. Wasif Nabeel Qureshi for their friendly time. Special thanks to my Ph.D. colleagues Dr. Hasneeza Lisa Zakaria and Dr. Abdullah for their help and time. I am grateful to Dr. Nomani Kabir for his kind help. I would like to give special thanks to Dr. Bestoun S. Ahmed and Dr. Mansoor for their valuable guidance and research tips. I am thankful to all my lab mates for their love and respect. I am grateful to all my colleagues especially to Dr. Nasir Rashid, Dr. Sehat Ullah, Dr. Aftab Alam, Mr. Anwar Ul Haq, Dr. Fakhre Alam and Dr. Zahid Khan from the Department of Computer Science & IT for their moral support. May Allah reward you and bless you all with the best health and more successes.

I express my gratitude to Ministry of Higher Education (MOHE), Malaysia for supporting my Ph.D. studies. Thank you MOHE for the all the support via the prestigious Malaysian International Scholarship (MIS). Also, I would like to thank MOHE for partially supporting my work and publications through FRGS grant entitled: A Reinforcement Learning Sine Cosine based Strategy for Combinatorial Test Suite Generation (RDU:170103). I am grateful to University Malaysia Pahang (UMP) for providing me the best infrastructure and conducive research environment and for supporting my studies. The time I spent at UMP is indeed the best time of my life which I will always remember. I would like to thank all faculty and staff members of Faculty of Computer Systems & Software Engineering (FSKKP) for their support and help. Last but not least, I am very grateful to the administration of University of Malakand for allowing me to pursue Ph.D. from Malaysia.

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ABSTRAK

Penggunaan algoritma meta-heuristik sebagai asas untuk strategi t-cara (di mana t menunjukkan kekuatan interaksi) dan ujian kekuatan bercampur adalah perkara lumrah dalam kajian masa kini. Kebanyakan strategi penjanaan data ujian adalah berdasarkan algoritma meta-heuristik seperti Simulasi Penyepuhlindapan (SA), Pencarian Tabu (TS), Algoritma Genetik (GA), Pengoptimuman Koloni Semut (ACO), Pengoptimuman Gerombolan Zarah (PSO), Pencarian Harmoni (HS), Pencarian Burung Kedasih (CS), Algoritma Kelawar (BA) dan Algoritma Lebah yang telah dibangunkan pada tahun-tahun kebelakangan ini. Walaupun banyak kemajuan telah dicapai, penyelidikan ke atas strategi baru masih relevan kerana tiada strategi tunggal dapat mendominasi strategi sedia ada (seperti yang diramalkan oleh Teori Makan Tengahari Percuma). Di samping itu, kajian meta-heuristik bebas parameter tidak diterokai sepenuhnya dalam literatur saintifik. Oleh kerana prestasinya yang terbukti dalam banyak masalah pengoptimuman lain, penggunaan algoritma Pengoptimuman berasaskan Pembelajaran Pembelajaran (TLBO) yang bebas parameter sebagai strategi t-cara baru dirasakan amat berguna. Tidak seperti algoritma meta-heuristik yang sedia ada, TLBO adalah bersifat bebas parameter, dan tidak mempunyai sebarang kawalan parameter tertentu. Oleh itu, TLBO menghindarkan keperluan untuk proses penalaan khusus yang rumit dan tertumpu hanya pada bermasalah tertentu. Walau bagaimanapun, TLBO mengambil pendekatan yang mudah untuk melakukan carian global dan setempat secara berurutan pada setiap lelaran. Memandangkan proses eksplorasi (iaitu mencari lokasi baru yang berpotensi di ruang carian) dan eksploitasi (iaitu memanipulasi kejiranan setempat) adalah bersifat dinamik dan bergantung kepada ruang carian semasa, mana-mana pembahagian tetap antara keduanya boleh menjadikan proses carian kurang berkesan. Menangani isu-isu ini, tesis ini mencadangkan variasi TLBO baru berdasarkan sistem inferensi kabur Mamdani, yang dikenali sebagai Adaptif TLBO (ATLBO), untuk membolehkan pemilihan operasi carian global dan carian tempatan yang adaptif. Sistem inferensi kabur Mamdani mempunyai tiga masukan: pengukur kualiti, pengukur eksplorasi, pengukur eksploitasi dan satu keluaran pemilihan. Tiga masukan ini merekod keperluan bagi mencapai nilai optimum dengan membimbing prosess carian ke arah yang betul.Pengukuran kualiti dan explorasi digunakan untuk mencapai kepelbagaian penyelesaian, sedangkan langkah Intensifikasi digunakan untuk memudahkan penumpuan. Output sistem inferensi kabur Mamdani bertindak sebagai suis berselang-seli untuk pemilihan antara operasi carian global dan carian tempatan. Penerapan ATLBO untuk strategi penjanaan kekuatan ujian t-cara campuran menunjukkan prestasi yang kompetitif dari segi saiz sut ujian yang diperolehi berbanding TLBO asal dan algorithma meta-heuristik yang lain. Secara kesimpulannya, ATLBO menunjukan pencapaian secara purata terbaik sebanyak 39 untuk sut ujian yang dijalankan dengan mengunakan data eksperimen penanda aras dan merupakan strategi bebas parameter pertama boleh menghasilkan kedua-dua bentuk sut ujian iaitu keseragaman dan kekuatan bercampur parameter t-cara.

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ABSTRACT

The use of meta-heuristic algorithms as the basis for t-way (where t indicates the interaction strength) and mixed strength testing strategies is common in recent literature. Many test data generation strategies based on meta-heuristic algorithms such as Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search (HS), Cuckoo Search (CS), Bat Algorithm (BA) and Bees Algorithm have been developed in recent years. Although much progress has been achieved, research into new strategies is still relevant owing to the fact that no single strategy can claim dominance over other existing ones (i.e., as stipulated by the No Free Lunch Theorem). Additionally, the adoption of new parameter-free meta-heuristic-based t-way strategies has not been sufficiently explored in the scientific literature. Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. Unlike most existing meta-heuristic algorithms, and by virtue of being parameter-free, TLBO does not have any specific parameter controls. Thus, TLBO avoids the need for cumbersome and problem specific tuning process. However, on the negative note, TLBO takes a simplistic approach of performing both global and local search sequentially per iteration. Given that exploration (i.e., globally finding the new potential region in the search space) and exploitation (i.e., locally manipulating best-known neighbourhood) are dynamic in nature depending on the current search space region, any preset division between the two can be counter-productive. Addressing these issues, this thesis proposes a new TLBO variant based on a Mamdani-type fuzzy inference system, called adaptive TLBO (ATLBO), to permit adaptive selection of its global and local search operations. The Mamdani-type fuzzy inference system of ATLBO has three inputs: Quality measure, Diversification measure and Intensification measure and one output: Selection. The three input measures capture necessary details so as to achieve optimality by guiding the search process in the right direction. Quality and Diversification measures are used to achieve solution diversity, whereas the Intensification measure is used to facilitate convergence. The Selection output of the Mamdani-type fuzzy inference system acts as an intermittent switch between global search and local search in ATLBO. The adoption of ATLBO for the mixed strength t-way test generation strategy demonstrates competitive performances in terms of obtained test suite sizes against the original TLBO and other meta-heuristic counterparts. To conclude, ATLBO-based strategy contributes to 39 new best average test suit sizes on benchmarking experiments and is the first

parameter-free strategy that addresses generation for both uniform and mixed strength t-way test

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TABLE OF CONTENT DECLARATION TITLE PAGE ACKNOWLEDGEMENTS ii ABSTRAK iii ABSTRACT iv TABLE OF CONTENT v

LIST OF TABLES viii

LIST OF FIGURES ix

LIST OF SYMBOLS xi

LIST OF ABBREVIATIONS xiv

CHAPTER 1 INTRODUCTION 1

1.1 Overview 1

1.2 Problem Statement 5

1.3 Aim and Objectives 9

1.4 Research Scope 9 1.5 Research Activities 10 1.5.1 Literature Review 11 1.5.2 Methodology 12 1.5.3 Benchmarking 12 1.6 Thesis Structure 12

CHAPTER 2 LITERATURE REVIEW 14

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2.1.1 Equivalence Partitioning 15

2.1.2 Boundary Value Analysis 16

2.1.3 Cause and Effect Graphing 16

2.2 Theoretical Background: Mixed Strength and t-way Testing 18 2.2.1 Mixed Strength and t-way Testing: A Motivating Example 18

2.2.2 Basics of Interaction Coverage 20

2.2.3 Mathematical Objects for Test Suites Representation 23

2.3 Meta-heuristic Algorithms 28

2.4 Meta-heuristic-based t-way Strategies 30

2.4.1 Simulated Annealing-based t-way Strategies 31

2.4.2 Tabu Search-based t-way Strategies 33

2.4.3 Genetic Algorithm-based t-way Strategies 35 2.4.4 Ant Colony Algorithm-based t-way Strategies 37 2.4.5 Particle Swarm Optimization-based t-way Strategies 38

2.4.6 Harmony Search-based t-way Strategies 40

2.4.7 Cuckoo Search-based t-way Strategies 41

2.4.8 Bat Algorithm-based t-way Strategies 43

2.4.9 Bees Algorithm-based t-way Strategies 44

2.5 Categories of Meta-heuristic-based t-way Strategies 45 2.6 Overview of Teaching Learning-based Optimization (TLBO) Algorithm 49

2.6.1 TLBO Variants and their Applications 50

2.7 Fuzzy Logic and Meta-heuristic Algorithms 52

2.8 Research Gap 53

2.9 Chapter Summary 57

CHAPTER 3 METHODOLOGY 58

3.1 The Original Teaching Learning-based Optimization (TLBO) Algorithm 58

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3.2.1 The Mamdani-type Fuzzy Inference System of ATLBO 62

3.2.2 The General ATLBO Algorithm 69

3.3 Computation of the Measures for t-way Testing 71

3.4 Implementation of ATLBO for the Mixed Strength t-way Test Suite Generation 72 3.4.1 Interaction Elements Generation Algorithm 73 3.4.2 Test Suite Generation Algorithm based on ATLBO 75

3.5 Chapter Summary 79

CHAPTER 4 RESULTS AND DISCUSSION 81

4.1 Experimental Setup 81

4.2 Characterizing Time and Size Performances for TLBO and ATLBO 84 4.3 Benchmarking with other Meta-Heuristic Strategies 85

4.4 Statistical Analysis 85

4.5 Discussion 86

4.6 Threats to Validity 106

4.7 Chapter Summary 108

CHAPTER 5 CONCLUSION AND FUTURE WORK 109

5.1 Objectives Revisited 109

5.2 Contributions 111

5.3 Future Work 112

REFERENCES 114

APPENDIX A LIST OF PUBLICATIONS 126

APPENDIX B BEST PAPER AWARD 128

APPENDIX C MALAYSIAN INTERNATIONAL SCHOLARSHIP 129

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LIST OF TABLES

Table 2.1 Decision Table for the Example in Figure 2.2 17 Table 2.2 The Online Gaming Architecture: Parameters and Values 19 Table 2.3 Pairwise and Mixed Strength Test Suite for the System in Figure 2.3 20 Table 2.4 A Simplified Example of a System with three Parameters two Values 21 Table 2.5 The Exhaustive Test Suite for the System in Table 2.4 21 Table 2.6 Mathematical Objects for t-way Test Suites and their Notations 24

Table 2.7 Standard Meta-heuristic-based Strategies 46

Table 2.8 Hybrid Meta-heuristic-based Strategies 47

Table 2.9 Adaptive Meta-heuristic-based Strategies 48

Table 2.10 Existing Straetegies based on Meta-Heuristic Algorithms for t-way Test

Suite Generation: Strengths and Weaknesses 55

Table 3.1 Fuzzy Rule Base of the ATLBO Fuzzy Inference System 67 Table 4.1 Parameter Settings for the Competing Meta-heuristic Algorithms 84

Table 4.2 Characterizing TLBO and ATLBO 88

Table 4.3 CA(N; t, 3p) 90 Table 4.4 CA(N; t, v7) 91 Table 4.5 CA(N; t, v10) 92 Table 4.6 VCA(N; 2, 315, {C}) 93 Table 4.7 VCA(N; 3, 315, {C}) 94 Table 4.8 VCA(N; 2, 43 53 62, {C}) 95

Table 4.9 Wilcoxon Rank-Sum Test for Table 4.2 100

Table 4.10 Wilcoxon Rank-Sum Test for Table 4.3 100

Table 4.11 Wilcoxon Rank-Sum Test for Table 4.4 101

Table 4.12 Wilcoxon Rank-Sum Test for Table 4.5 101

Table 4.13 Wilcoxon Rank-Sum Test for Table 4.6 101

Table 4.14 Wilcoxon Rank-Sum Test for Table 4.7 102

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LIST OF FIGURES

Figure 1.1 Failure and Software System 2

Figure 1.2 Relationship of Errors, Faults and Failures 3 Figure 1.3 Abstract Level Representation of Combinatorial t-way Testing 4 Figure 1.4 Strategies with/without Parameter Tuning for the Problem of t-way Test

Suite Generation 7

Figure 1.5 Performance Issues with the Original TLBO Algorithm 8

Figure 1.6 Research Activities 11

Figure 2.1 A Simple Application to Illustrate Equivalence Partitioning 15

Figure 2.2 The CEG for the Example in Figure 2.1 17

Figure 2.3 Online Gaming Architecture 18

Figure 2.4 Total Pairwise Interaction Tuples for the System in Table 2.4 22 Figure 2.5 Interaction Elements/Tuples Coverage for the System in Table 2.4 23

Figure 2.6 Representation of CA, MCA, and VCA 28

Figure 2.7 General Meta-heuristic-based Strategy for t-way Test Suite Generation 30 Figure 2.8 SA-based Strategy for t-way Test Suite Generation 33 Figure 2.9 TS-based Strategy (MiTS) for t-way Test Suite Generation 35 Figure 2.10 GA-based Strategy for t-way Test Suite Generation 36 Figure 2.11 ACA-based Strategy for t-way Test Suite Generation 38 Figure 2.12 PSO-based Strategy for t-way Test Suite Generation 40 Figure 2.13 HS-based Strategy (HSS) for t-way Test Suite Generation 42 Figure 2.14 CS-based Strategy for t-way Test Suite Generation 43 Figure 2.15 BA-based Strategy (BTS) for t-way Test Suite Generation 44 Figure 2.16 Bees Algorithm-based Strategy for t-way Test Suite Generation 45

Figure 2.17 Types of TLBO Variants 50

Figure 2.18 Research Problems in the Existing Related Literature 56

Figure 3.1 Concepts of TLBO for Optimization 59

Figure 3.2 TLBO's Teaching and Learning Analogy 60

Figure 3.3 The Original TLBO Algorithm 61

Figure 3.4 Fuzzy Inference System for ATLBO 65

Figure 3.5 Membership Functions of the three Input Measures 66 Figure 3.6 Membership Functions of the Selection Output Linguistic Variable 66 Figure 3.7 Max-min Inference Method and Defuzzification 68

Figure 3.8 ATLBO based on Fuzzy Inference System 70

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Figure 3.10 Hamming Distance Calculation for Intensification and Diversification 72 Figure 3.11 The Hash Map and Interaction Elements for the VCA 73 Figure 3.12 Algorithm for Interaction Elements Generation 74 Figure 3.13 ATLBO for Generating Mixed Strength t-way Test Suite 77 Figure 3.14 Graphical Representation of Test Suite Generation by ATLBO 78 Figure 3.15 Example for Illustrating Generation of Test Suite and Removal of

Interaction Elements from Hs 79

Figure 4.1 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.2 88

Figure 4.2 Box Plots for Table 4.2 89

Figure 4.3 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.3 96 Figure 4.4 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.4 97 Figure 4.5 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.5 98 Figure 4.6 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.6 99 Figure 4.7 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.7 99 Figure 4.8 Mean Exploration and Exploitation Percentage of ATLBO for Table 4.8 100

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LIST OF SYMBOLS ∑ Summation ! Factorial λ Lamda ∃ There exists + Don’t care % Percentage P Number of parameters

v Values each parameter carries

t Interaction strength

N Number of rows

vP Number of parameters each carries v values

CAi ith covering array

Cost(CA) Cost of covering array

f(x) Objective function value (total interaction elements covered) xi Single interaction element

∈ Element of

Hs Hash map of interaction elements

RM Ringgit

/ Division

≥ Greater than or equal to ≤ Less than or equal to

⊇ Superset or equal to

𝐴′ New solution

𝑓(𝐴′) Fitness function

𝑝 Acceptance probability

𝑟 Cooling rate

∆ The difference in fitness functions

ε Maximum evaluations

so Initial solution

s* New solution

𝜌 Neighborhood function

𝜎 Number of elite elements

Ʈ Pheromone amount

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α Pheromone coefficient β Heuristic coefficient

ρ Pheromone evaporation rate Xi Candidate test case

gBest Global best

lBest Local best

C1, C2 Cognitive parameters

ω Inertia weight

Ps Combination list

Vit Velocity of ith particle at time t

𝑟𝑎𝑐𝑐𝑒𝑝𝑡 Acceptance rate

𝑟𝑝𝑎 Pitch adjustment rate

¬ Not

pa Probability of finding cuckoo eggs in a nest

Qi Frequency of bat

n Number of scout bees

m Number of patches

e Number of elite patches

Exp Exponent

C(c*) Cost of new solution

C(c) Cost of current solution D Dimension of the problem Xi Vector with D elements

𝑋𝑡𝑒𝑎𝑐ℎ𝑒𝑟 Best learner in population X

𝑋𝑚𝑒𝑎𝑛 Mean of population X

≠ Not equal to

X Population

X´ Updated population

TF Teaching factor

Xbest Current best solution in population

Im Intensification measure

Dm Diversification measure

Qm Quality measure

Xcurrent Current solution

μ(A(x)) Membership function value of fuzzy set max_fitness Maximum fitness

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min_fitness Minimum fitness

— Division

| Such that

| | Absolute value

∅ Empty set

tsub Sub strength

α Significance level

𝛼𝐻𝑜𝑙𝑚 Bonferroni-Holm correction

* Best value

- Result not available

∏ Product

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LIST OF ABBREVIATIONS

ATLBO Adaptive Teaching Learning based Optimization TLBO Teaching Learning based Optimization

GA Genetic Algorithm

SA Simulated Annealing

PSO Particle Swarm Optimization

ACO Ant Colony Optimization

HS Harmony Search

DPSO Discrete Particle Swarm Optimization APSO Adaptive Particle Swarm Optimization

BA Bat Algorithm

CS Cuckoo Search

TS Tabu Search

ACA Ant Colony Algorithm

FPA Flower Pollination Algorithm

PSTG Particle Swarm-based Test Generator

CEG Cause and Effect Graphing

ILS Iterated Local Search

VNS Variable Neighborhood Search

GLS Guided Local Search

GRASP Greedy Randomized Adaptive Search Procedure CASA Covering Arrays for Simulated Annealing

OA Orthogonal Array

CA Covering Array

MCA Mixed Covering Array

VCA Variable Strength Covering Array CCA Constrained Covering Array

SCA Sequence Covering Array

CTCA Cost-Aware Covering Array

OPAT One-Parameter-At-A-Time

OSAT One-Set-At-A-Time

OTAT One-Test-At-A-Time

IEEE Institute of Electronics & Electric Engineering BSOD Blue Screen of Dearth

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UML Unified Modelling Language

GCC GNU Compiler Collection

NP Nondeterministic Polynomial time CPHF Covering Perfect Hash Families

MiTS Mixed Tabu Search

TSA Tabu Search Algorithm

GS Genetic Strategy

VS-PSTG Variable Strength Particle Swarm-based Test Generator CPSO Conventional Particle Swarm Optimization

HSS Harmony Search-based Strategy

HM Harmony Memory

HMS Harmony Memory Size

BTS Bat-inspired t-way Strategy QOBL Quasi Opposition Based Learning

FATLBO Fuzzy Adaptive Teaching-Learning-based Optimization MTLBO Modified Teaching-Learning-based Optimization ITLBO Improved Teaching-Learning-based Optimization

DE Differential Evolution

HSTLBO Harmony Search-based Teaching-Learning-based Optimization Co-TLBO Cooperative Teaching-Learning-based Optimization

FL Fuzzy Logic

COG Centre of Gravity

FWER Family-Wise Error Rate

GUIs Graphical User Interfaces ISA Improved Simulated Annealing

SA-VNS Simulated Annealing and Variable Neighborhood Search ITL Interaction Tuples List

IEL Interaction Elements List FTLS Final t-way Test Suite

FTS Final Sequence t-way Test Suite

IE Interaction Element

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Figure

2.4.2  Tabu Search-based t-way Strategies  33

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